From Data to Decisions: Enhancing Retail with AI and Machine Learning

Pan Singh Dhoni ()

International Journal of Computing and Engineering, 2024, vol. 5, issue 1, 38 - 51

Abstract: Purpose: The recent advancements in computational power have presented unprecedented opportunities for businesses to harness data. A noteworthy development in December 2022 was the introduction of OpenAI's [1] ChatGPT, signifying the rise of generative AI tools including, but not limited to, Bard [2], Midjourney [3], GitHub Copilot [4], Amazon Bedrock, and Google's Gemini [5]. This research paper aims to harness AI capabilities within retail organizations, using data (customer) to expand business reach and enhance customer satisfaction. Data and AI form the core of this research. Methodology: In this research, we have trained a Large Language Model (LLM) by providing it with database schemas, including tables, to interact with centralized data and gain insights through simple prompts. We can leverage data for data Analysis and create reports, dashboards, understand customer behavior. Findings: Our research findings that AI serves as a pivotal force in amplifying the retail industry's potential. AI's applications span from improving customer experience by enabling voice orders, emotional insight, exclusive Deals just for you, product design, email campaign, optimizing inventory to facilitating targeted marketing strategies, list goes on. Yet, as we navigate this AI-augmented retail landscape, it is imperative to address challenges related to data privacy, algorithmic bias, implementation costs, and the need for expertise. Unique contributor to theory, policy and practice: In essence, generative AI is more than a fleeting trend; it epitomizes the future of retail, demanding both adoption and circumspection. Our recommendation to use AI for enhancing retail business and use it ethically.

Keywords: Analytics; Customer Data; Generative Ai; Machine Learning; Retail. (search for similar items in EconPapers)
Date: 2024
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